Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4838
Title: Identification of full-night sleep parameters using morphological features of ECG signals: A practical alternative to EEG and EOG signals
Authors: Yücelbaş, Şule
Yücelbaş, Cüneyt
Tezel, Gülay
Özsen, Seral
Yosunkaya, Şebnem
Keywords: Random subspace algorithm
Sleep parameters
ECG
Morphological features
Heart-Rate-Variability
K-Complexes
Automated Recognition
Spindle Detection
Blood-Pressure
Time
Slow
Amplitude
Arousal
System
Issue Date: 2024
Publisher: Elsevier Sci Ltd
Abstract: Electroencephalogram (EEG) signals, which are among the most important recordings used in Polysomnography for sleep staging, are more challenging and demanding than electrocardiography (ECG) signals, both in terms of acquisition and interpretation. When examining the studies of other researchers on sleep parameters in the literature, it is evident that EEG signals are predominantly used for determining arousal (AR), K-complex (Kc), and sleep spindle (Ss) parameters. Furthermore, it is understood that electrooculography (EOG) signals are employed for detecting slow eye movements (SEM) and rapid eye movements (REM) parameters.This study is a continuation of our previous research, where we used only EEG signals for Kc and Ss detection. In this study, an approach that includes ECG signals in the determination of sleep parameters to bring practicality to sleep staging studies was adopted. For this purpose, firstly, 16 morphological features were extracted from ECG recordings taken from a total of 24 subjects after various preprocessing steps. Subsequently, these data were used to work on the detection of five different sleep parameters: AR, Kc, Ss, SEM, and REM, using the Random Subspace (RaSE) ensemble learning algorithm. The results were calculated according to various statistical criteria and a classification accuracy of over 78 % was obtained in all parameters. As a result, the sleep parameters that could be determined most successfully using the ECG signal were SEM and arousal, respectively. In addition, feature elimination was performed for these datasets using Symmetric Uncertainty (SU) ranking. As a result of the reclassification process using 9 and 12 features, the effectiveness of which was determined for both datasets, respectively, significant increases were observed in the performance outputs. Experimental results have shown that ECG signals can be used as an alternative to EEG and EOG signals in the determination of full-night sleep parameters.
URI: https://doi.org/10.1016/j.bspc.2023.105633
https://hdl.handle.net/20.500.13091/4838
ISSN: 1746-8094
1746-8108
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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